@版本 2.0.0
譯注:此翻譯版,主要給不能流利的讀英文的人看,相關專有名詞還是保留原文。翻譯不好地方請協助pull request.
此repository包含了一些前端開發的面試問題,來審查一個有潛力的面試者。這並不是建議你對同一個面試者問上所有的問 (那會花費好幾小時)。從列表中挑幾個題目,應該就夠幫助你審查面試者是否擁有你需要的技能。
Rebecca Murphey 的 Baseline For Front-End Developers 也是一篇很棒且值得讀的文章在你開始面試之前。
@版本 2.0.0
譯注:此翻譯版,主要給不能流利的讀英文的人看,相關專有名詞還是保留原文。翻譯不好地方請協助pull request.
此repository包含了一些前端開發的面試問題,來審查一個有潛力的面試者。這並不是建議你對同一個面試者問上所有的問 (那會花費好幾小時)。從列表中挑幾個題目,應該就夠幫助你審查面試者是否擁有你需要的技能。
Rebecca Murphey 的 Baseline For Front-End Developers 也是一篇很棒且值得讀的文章在你開始面試之前。
| <html> | |
| <head> | |
| <meta name="viewport" content="width=device-width; initial-scale=1.0; maximum-scale=1.0; user-scalable=0;"> | |
| </head> | |
| <body style="margin:0px;padding:0px;overflow:hidden"> | |
| <iframe src="__URL_HERE__" frameborder="0" style="overflow:hidden;overflow-x:hidden;overflow-y:hidden;height:100%;width:100%;position:absolute;top:0px;left:0px;right:0px;bottom:0px" height="100%" width="100%"></iframe> | |
| </body> | |
| </html> |
Service Worker - offline support for the web
Progressive apps - high-res icon, splash screen, no URL bar, etc.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.